HOME QUICK SEARCH AUTHORS KEYWORDS HELP BUGS

SUGI 1980 Papers - Index

88 papers. Source: SAS Users Group International conference proceedings.

Back to Conf
go 1981
go 1979
# Title
×		 Authors
×		 Abstract
×		 Keywords
×		 Extras Pages
1 A Characterization of the GLM Sums of Squares Ronald R. Hocking,
F. Michael Speed		 proc genmod, anova source 4
2 A Direct Access Method for Information Retrieval from SAS Data Bases Frank C. Dilorio,
Kenneth A. Hardy,
James D. Hosking		 proc step, sample source 5
3 A Macro for Multivariate Randomization Analyses of Stratified Sample Data Ingrid A. Amara,
Gary G. Koch		 covariance, randomization, response variable, anova, missing value, ancova, sample, proc step, permutation test, multivariate analysis, proc freq, p-value, proc genmod, chi-square test, sample size, data science, imputation, correlation source 11
4 A Measure of Predictive Performance for Estimation When the Predicted Value is a Probability Josefina A. Lago Traditionally, the RL statistic is used as the measure of predictive pre formance. However, when the estimation sample, predictor variable, response variable source 6
5 A Methodology for Capacity Planning Mary Sue Dickerson,
Franklin S. Young		 capacity planning, cpu time source 5
6 A Mid-Range Raw Materials Planning System Harold W. Creech,
Patricia A. Hermes		 dataset, update statement source
7 A Nonprocedural Query Facility for the Casual SAS User Arnold W. Bragg Jr. proc step, relational database source
8 A SAS Coloring Book as a Teaching Aid Franklin S. Young proc step, data wrangling source 6
9 A SAS Macro for Computing the Kappa Statistic to Assess Reliability John Helzer,
John Rice,
Edward L. Spitznagel		 correlation, cohen's kappa, null hypothesis, proc calis, sample, macro definition source 5
10 A SAS Macro for Evaluation of Regression Experimental Designs Paul J. Von Doehren regression analysis, sample source
11 A Statistical Report Generator System Using SAS as Foundation Software Misa Gratton data governance, length statement source
12 An Overview of Survival Analysis and SAS Irving K. Hwang regression analysis, likelihood function, predictor variable, survival analysis, logistic distribution, life table, maximum likelihood estimation, proc rank, response variable, logistic regression, proc step, discriminant analysis, log-rank test, relative risk, wilcoxon test source
13 Analysis and Presentation of Student Evaluations of Faculty Data Using SAS Richard K. Ford,
Bruce R. Lewis,
Steven F. Thomson,
Kenneth E. Wagner		 Eastern Kentucky University has recently endorsed Student Evaluations of Faculty (SEF) as a concept. However, as with any agreement in principle, the mechanics of actually implementing such a plan creates many problems. Two of the concerns expressed by the faculty in discussing the principle of SEF were the method of analyzing the results and the use of the results. The faculty was in almost total agreement that the evaluations would be worthwhile if they provided feedback to the instructor and were not used in promotion decisions. Therefore, having the evaluation results for each faculty member compared only to his own efforts was a desirable quality of the analysis process. proc step, dataset source 3
14 Analysis of a Fragmented Time Series Using GLM James E. Dunn degrees of freedom, proc genmod, sample, time series analysis source
15 Analysis of Repeated Measures Experiments: One- and Two-way Classifications Louis Bonus,
Harji I. Patel		 This paper modifies Holzer and Conlonls (1978) procedure for a split-plot type analysis of repeated measures designs. The split-plot repeated measures, covariance, ancova, proc genmod, proc glm, proc step, univariate analysis, likelihood function, compound symmetry, response variable source 6
16 Another Application of Stepwise Multivariate Analysis Using SAS Chandu M. Patel multivariate analysis, main effect, covariance, proc step source 6
17 Authoring an Interactive SAS Course Ione H. Cockrell,
Dorsey A. G. Condon		 data science source 5
18 Automated Analysis of Case-Control Data Using the SAS System Elizabeth J. Martin,
Linda Williams Pickle		 odds ratio, relative risk, logistic distribution source 6
19 Box-Jenkins Time Series Method and Computer Packages Syed Shahabuddin time series analysis, correlation, moving average, proc arima, probability distribution source 5
20 CertainTeed Corporation's SAS Training/Support Program Myron S. Kaplan access control, ledger source 6
21 Clinical Data Management and SAS79 Michael S. Lajiness dataset, cpu time source 4
22 Computer Generated Repeatable Examinations Edward L. Spitznagel For a number of years, we have used a take-horne examination system that combines some of the best features of homework assignments and in-class examinations. Each student receives a set of unique problems to be completed within a period of several days. Because no two students receive the same problem, grading can be based on the anSwers alone, and the students can be given several tries to work the problems correctly. The instructor has the advantage of asking ;ealistically hard problems, and the stud cnts have the advantage of being able to work on them without the time pressure usually induced by in-class examinations. This standard deviation, likelihood function source 5
23 Conversational Data Input System: An Interface between VS BASIC and SAS Barbara Forsyth,
John David Rumpel		 The purpose of an information system is to provide decision makers with facts relevant to important issues~ The generation of this crucial information usually requires individuals with 8pecia11zed training in data entry. The present paper describes a user-oriented inter active information system. Utilizing the Com mand List (C-List) facility in Virtual Storage Personal Computing (VSPC), input processes are simplified and pertinent statistical infor mation is provided. The input program. written in VS BASIC, creates a raw data file appropriately formatted for use by SAS. Through SAS~ the new data file is merged with a master file and the desired statistics are produced by appropriate SAS procedures. Both the BASIC and SAS programs are accessed by on~ command which activates the C-List. The system described here is currently employed by the Social and Behavioral Sciences Laboratory at the University of South Carulina to monitor the utilization of satellite facili ties data science, dataset, usage data source 5
24 Cross-Classification Matrix Output Program James H. Kuzirian 5tat~ HIghway Hi [eage data~ givi~g the number of mIles of existing roadw~y for all publ io roads. was clas5ified by type of ~id program, functional 'SY:'Item. maintenance responsibility. and wh~ther ru,..~1 or urb<l.t'l. The r-esutting tross-olassifle~tion required an outp~t grid of 28 rous and 16 columns to pres9nt th&-data. Ho~ ever, the data is- quite spar-se, wit.h typically 10-20% of the cells for a given county having any data. Tnerefore. a row and column Index W~$ com puted for e.ach d<:lta value. to IOcat£) it on the output page. using the ~'N~PS" ~ful! page) option under the FILE statement, Row and ~olumn head in95 w~re generated in an an~logous fashion. This system. including 6 S'!'S progratns. was writte-n. in 'S-AS76.6D; it lJould've been easier and snorter to program in SA579. The paper describes the report writing program in detai I. whi~n uses the follot..Jin!l techniques: structured program nllng. I inK routines. H=PS.. FIRST. and LAST proc step, proc sort source 6
25 Development of an Interactive Weibull Data Analysis System Utilizing SAS John B. Cosgray proc step, by group processing source 5
26 Efficiency Comparison of SAS with PL/I for Programming a Client Monitoring System in a Community Mental Health Center C. William Cox Jr.,
Cynthia Deitz		 WbBn an institution r~lies V~rU he~vilg on 5AS for its ~eporting l'equiT"~ments~ the qUlistion a-risss as to the efficiency of SAS compared to a high l~v&l p~ogramming language. It is obviQUS that the ease 0' generating output with BAS utililing its fC1"'matting. merging, and sQrting (ap~bilitie5 is ver~ 9~Dnomical jn the short l"un< S-AS is also economical when output will require m~ny periodic fermat changes. How~v~r, if th~ pTQgrsmming requirement~ ar~ ye~g co~pl~x, r.~viTe much data manipulation~ and will bE' -u$ed over a long period of time~ ~ program wTitten in a high l~v@l languaQa might be more ~f'icient. A client monitoring 5y5t~m Fo~ ~ Community Mental Health Cent.~ which WqS being written in PL/1 !lad to b~ re-written using SAS due to staffing chenge~ ~nd tight deadlines. lhis situation provided an opportunity to contr~gt a 5ij$t~m written in SAS with the ~ame ,y~tem developed in PL/I source 6
27 Evaluation of Discount Air Fare Plans R. A. Erickson,
F. K. Kuiper		 proc step, proc glm, dataset, proc means, anova source
28 Examples of GLM Applications Ramon C. Littell proc genmod, regression analysis, proc glm, proc step, estimate, anova, confidence interval, residual source 7
29 Fitting Time Series Using SAS David M. DeLong correlation, proc cancorr, covariance, sample, autoregressive model, model training, time series analysis source 5
30 FMTLIB: A SAS Procedure to Generate a Listing of the Contents of All Formats in a Library Richard D. Langston proc step, proc format source 5
31 Formatted Displays Using Output from PROC SYSREG M. W. Harrell,
P. A. Reid		 regression analysis, proc step source 5
32 Formatted Tables Using SAS James N. Arvesen,
Stephen F. Follmer,
Mark V. Shashihara		 proc step, dataset source
33 Frequency Distributions of Randomly Arranged Values in a SAS Data Set with over a Million Observations Robert L. Clements,
Richard A. Fleenor		 Every week during a clinical animal experiment various observations relating to the condition of the anima19 ar~ recorded. IheT~ are a total of 68 possible conditions that may be observed on any giv""'n animal e~ch .... eek~ Each of these 68 conditions are assign~d a unique character code. these codes are then recorded on the live animal data base in the order that the care:::akers ovservEd then. Up to l8even of these codes are observed each week for each animal. Over the duration of the EnOl I".xperiment conducted at the National Center for Toxicological Research, the number of cha~act(:'r strings (animal-weeks) reflecting the condition of the animals exc~edcd two million. This pape~ de8cribes the approa~h utilized to obtain the frequency d istr ib ution of the se animal cond i tion codes proc freq, macro definition source 7
34 Further Properties of the Pair Chart John L. Stedl Within the framework of a pair chart the random variable In is defined to be the number of intersections with the 45° line thru the origin. The distribution of In is given along with its use in testing Ho: F = G~ The results are for the equal sample size ~ase only. An example is given. Introduc tion Sinha and Hardy (1), at the 19 79 SUGI meet ing, presented a SAS macro for plotting the "pair chart" which provides a graphical comparison of two variables in the two-sample problem~ The pair chart also provides a convenient format for computing the Uann-Whi tuey and other nonparamet ric statistics. This paper presents, in the equal sample size easet the distributional prop erties of the number of intersections with the 45° line thru the origin in a pair chart. This is of special interest since this 45° line repre sents equality of the two distributions sample, sample size source 1
35 Guttman Scale Analysis David C. Blouin proc freq, residual, likelihood function, correlation source 4
36 Implementation of an Automated Survey Control System Using SAS Morse F. Kalt sample, proc freq source 5
37 IMS Network Analysis Program Patrick A. Drayton source 3
38 Interfacing SAS with RAPID (Relational Access Processor for Integrated Databases) Herbert L. Cover The Bureau of Labor Statistics (BLS) will be responsible for processing, updating, analyzing, and publ ishing a large quantity of data call ected quarterly through the Cant iRuing Consumer Expenditure Survey (CCES). The data base will be stored categorically into a family information file, an individual family member characteristics file, and expenditure group files such as clothing~ food, insurance, etc. Each file, or relation. can be viewed as a dense matrix, in which a row is present only if a family is represented, and the col umns are the correspona ing attributes. Each re 1 at ion will have a common attribute, the family identification number, to allow information linkage. The structure of this database appears to be well suited for RAPID, a database manage ment system developed by Statistics Canada adapted to the "relational II model first popularized by E.F. Codd of I.B.M. Each matrix is stored by col umn rather than row, allowing efficient retrieval of selected attributes dataset, proc step, variable, format statement, sample source 6
39 Interfacing SAS with Simulation Software to Enhance Computer Performance Evaluation Arnold W. Bragg Jr. sas output, likelihood function source 5
40 Jacknife Techniques in Regression Analysis Anthony A. Billings,
Daniel M. Chilko,
Gerald R. Hobbs,
Paul Hshieh,
Ronald D.King		 regression analysis, sample, proc step, data cleaning source 5
41 Least Squares Means in the Fixed Effect Analysis of Variance Kenneth L. Koonce,
F. Michael Speed		 least squares mean, proc genmod, main effect, anova, sample, proc step, proc freq source
42 Management and Analysis of Data from an In-Use Fuel Economy Test Gwendolyn H. Walton sample, anova source 3
43 Management and Performance Reporting in an MVS Environment Mario M. Morino data science, data wrangling, histogram, correlation source
44 Measuring CPU Power via Hardware Monitor Terry L. Flynn sample, proc freq source 3
45 Miss America in Review George L. Miller source 10
46 MRR: A New Approach to Ridge Regression Using SAS T. Janakiraman,
Lawrence C. Marsh		 regression analysis, ridge regression, mse, proc reg, sample source 5
47 n Annotated Guide to the Analysis of Linear Models Using SAS Rudolf J. Freund,
Ramon C. Littell		 proc genmod, regression analysis, proc calis, sample, proc glm, predictor variable, anova, proc step source 6
48 Nonmetric Multidimensional Scaling Under SAS Robert M. Hamer,
Rostyslaw Lewyckyj,
Forrest W. Young		 proc step, missing value source 5
49 Nonorthogonal Analysis of Balanced Data Dale O. Everson,
R. Kirk Steinhorst		 anova, main effect, proc mixed, proc genmod, likelihood function source 5
50 Odds Ratio Estimation for Polychotomous Exposures Elizabeth J. Martin,
Linda Williams Pickle		 odds ratio, likelihood function source 3
51 On-line Documentation and Tutorial for SAS Jan M. Hanley,
Franklin S. Young		 proc step, input statement source 4
52 On-line SAS Sample and Support Library Alan Wren Maloney sample, proc step source 7
53 Panel Discussion: The Role of the SAS Consultant in a User Community Mason B. Nichols,
William L. Sanders,
Daniel B. Shapiro		 sas consultant, data governance source
54 Pre-programmed Routines for an Applied Linear Models Course James A. Calloway,
William L. Seaver		 outlier, regression analysis, ridge regression, sas output, normality test source 6
55 PROC ITEM - An Item Analysis Procedure Cynthia Deitz,
David A. Smith		 The Procedure ITEM analy xes i t£!1n data from multiple--l'E:'sponse tests which have a single correct response ~or each item. A KEY statement which indicatp.s the correct response is used to compute a score for each individual. lObe output consists of four parts: 1) Total test statistics which includes the mean. median, .tand~rd deviation, nomogenity reliability KR-2i, standard error of measurement., possible low and high score, obtained low and high Score. and number of scores with counts of blank, valid, and invalid 61:01'&S. 2) A test f:requen.cy distribution 1:5 presented which indictes th~ raw score, standard score, p~rcentile rank~ percentage of people in total group who received the given score, fT'equenclj' cumulativE' freltllE?ncy, and a histogram portraying the frequency of the scores at ea~h score valwe. 3) An item quintile t~ble which compares the item response verses the total score distribution for each item proc freq, sample, proc step source 5
56 PROC MATPAR: A SAS Procedure for Calculating Matched-Pair Partial Correlations Robert E. Johnson,
Dana Quade		 correlation, sample, by group processing, proc step source 6
57 PROC PUTSPEK, a SAS to Speakeasy Interface John W. Davison Jr. proc step, dataset source 6
58 PROC SURVFIT: A Procedure for Estimating Survival Curves Using Kaplan-Meier or Actuarial Methods Glenn A. Augustine,
Thomas R. Fleming,
Kenneth P. Offord,
William F. Scott		 life table, calendar year, numeric variable, proc step source 7
59 Procedure TABLES: A Two-way Table Generator with Statistics for Control Versus Treated Comparisons George F. Fraction,
S. Stanley Young		 hypothesis testing, sample, proc step source
60 Procedures for Large Regression Problems Requiring Maximum Likelihood Estimation Frank E. Harrell Jr.,
Kerry L. Lee,
Ray A. McKinnis		 This paper deals with methods of handling large regression problems that require iterative maximum likelihood calculations. Ways of fitting a single model or of selecting variables by using forward stepwise or backward elimination algorithms are surveyed. Two supplemental SAS procedures using these methods (PHGLM for Cox Proportional Hazard General Linear Model and LOGIST for logistic binary regression) are discussed. likelihood function, response variable, regression analysis, logistic distribution, proc step source 4
61 Quality Assurance Controls in Research Data Base Management: Nonsense Codes in Hierarchical File Structures Steven P. Cobb,
Karen L. Daniels,
Michael P. Farrell,
A. Dale Magoun,
C. H. Pennington,
Harold Schramm,
Rodney H. Strand		 In complex studies using multip1e data bases composed of hierarchical file structures. there is a high probability that errorS may be perpetuated into summary reports unless some form of quality assurance is integrated into the research data base management program. In studies that substftute numeric codes for variable values, this problem of error ~ropa gation is even 1I10re acute. This paper add.ress es the problem of error propagation in those studies employing a coding scheme to re~resent longer alphanumeric values. Several approaches are available that mini mize errOrS in coding var1ables~ Nurreric codes with embedded information allocated to )ositions within the va1ue codes are widely used but unac ceptable for varlables with many values and/or many levels of classification. Such "smartll codes n2quire a full knowledge of the universe the variables describe as well as the potential classification schemes for each variable sample, quality assurance, proc format, quality control, proc freq, proc step source 5
62 Recent Developments in BMDP of Special Interest to SAS Users James W. Frane Several new features and modules have been added to SMOP since I addressed the $AS Users Group in 1978. The 1979 edition of the BMDP manual has been published. Many new documents have appeared. The latest version of SMOP is dated November 1979. This ¥ersion has been distributed to all licensed SMOP recipients that have IBM and similar systems. The SAS-BMDP interface was improved and te,ted before the rel.ase. The special 5AS-BMDP interface makes it especially easy for ,SAS users to execute BM~P programs with SA5 f,les. The value of th,s interface is evidenced by its many users. B~DP continues to grow and to supplement procedures found in 5AS. Several new programs and features are also under de¥elopment, anova, covariance, sample, regression analysis, repeated measures, proc mixed, ancova, histogram, response variable, maximum likelihood estimation, time series analysis, correlation, likelihood function, statistical software, proc step, missing value, residual, proc freq, proc genmod, logistic distribution, proc reg source 8
63 Regional Ecological Analysis and Data Base Applications Martha K. Nungesser,
Richard J. Olson		 proc contents, data governance source 6
64 Regression with Linear Splines and Plateaus Samuel G. Carmer,
Wen T. Hsieh,
William M. Walker		 regression analysis, predictor variable, response variable, polynomial regression source
65 SAS as a Central Statistical System Peter Beutel sample, missing value, proc step source 4
66 SAS as a Clinical Registration System Frank J. Potter The design and impl,""lIIt::llL~.:..ivn uf SAS 7942 as au .1Llformation system for the reg:i.t;ltration of volunteers at the Clinical Pharmacology Unit of Jefferson University Hospital is pres~nted in two parts. The first section describes the data base structure, updating procedures and report generation facilities. Concluding the presentation is a discussion on the instruction package and training given to the registry ~ys~em users. Following input from a minicomputer data entry and validation system; two record types are used to create the SAS files, with the first file containing information on demographic material of both former and current volunteers. The second file is dedicated to information generated from a ~linical trial, with each observation pertaining to a particular s~udy for a uniquH volunteer~ To facilitate the use of queries, at the c.onclusion of the update program to this data base a subroutine -is .called which proc step, clinical trial source 4
67 SAS as a Management Tool for Endangered Species Data Joshua Sharlin Over the years, basic life history data (e.g., date of birth and death, parent age and sex) of several subspecies of leopards has been maintained manually in studbooks. A studbook is basically a list of animals that are or have been in captivity~ Using SAS, information such as age of first reproduction, seasonality of births t average litter size and age distribution of the captive population over years have been calcu lated. This data is helpful in managing individuals in captivity and evaluating the status of the entire captive population proc step, histogram source 4
68 SAS at the Phila. Fed: Sequential, Hierarchical and Relational Data Structures Jack Siler proc means, bar chart source 5
69 SAS Functions to Compute Chi-Squared, F, and T Distributions and Their Inverses Allowing for Noncentrality and Fractional Decrees of Freedom C. David Hardison,
Richard D. Langston,
Dana Quade		 degrees of freedom, sample, f distribution source 4
70 SAS Macros for Survival Analysis James A. Bolognese,
Irving K. Hwang		 Three SAS macros SURV, LIFE. and LTGRAPH for survival analysis in a two-group situation are presented. Macro SURV calculates the chi-square test statistics for both the modified Mantel-Haenszel procedure and Peto' s logrank test. Adjusted relative death (or event occurrence) l~ates are also computed. The life tables based on the product-limit estimate of Kaplan and Meier are given by Macro LIFE and then plotted by LTGRAPH. A numerical example is given for illustra- tion life table, log-rank test, in=, likelihood function source 6
71 SAS Sample Selection Macros James R. Chromy,
Rick L. Williams		 sample, sample size, dataset, stored macro source 5
72 Scientific Data Management and Analysis Steve Dassin,
John J. Frey,
George F. Griffin,
John McKenna		 proc step, sample, data science, put statement, clinical trial, proc freq source
73 Service Level Capacity Planning: A Tough Nut to Crack Carlton L. Arnold Today I s management is demanding that data processing decisions be based on the quality of service delivered to the custorner~ This demand has impacted the area of capacity pI anni n9 to the poi nt where many camp an; es are recognizing that hardware plans must satisfy specified customer service levels. As a result, those companies are seeking a "capacity planning system" that is service lev,e] driven; however, it is the contention of this author that such a system does not edst today. Why doesn't one exist? This paper will identify some of the problems and constraints that have prevented the development of a complete capacity planning system that ;s service level driven capacity planning, cpu time source 7
74 Shaded Map Reports Edward L. Spitznagel dataset, missing value source
75 SHAPE: An Interactive Graphics SAS Procedure for Fitting Histograms Robert N. Rodriguez SHAPE. an acronym for ~mcoth Mistogram ~proximation by ~aramet~r ~stimation. i~ a~ interactive graphics SAS procedure for f1tt~ng histogram data with Burr III density curves~ The procedure can fit most histograms commonly en countered,. and the fitted curve has a simple functional form which caD then be used to eValu8t~ probabilities and percentiles or to build mathe matical models. The. program locates th~ ekewnesEl and kurto sis of the data--either grouped or ungrouped--on a moment ratio diagram. This enables the user to deterElne the suitability of a Burr III fit and to follow an iterative graphical version 'of the histogram, proc step, sample, proc freq source 6
76 Slope Ratio Bioassay Using SAS Janet E. Field,
Rudolph S. Parrish,
Glenn O. Ware		 regression analysis, anova, data science, multiple regression, numeric variable, proc calis, macro definition, proc reg source
77 Some Steady State Queueing System Macros William R. Gjertsen macro definition, proc step source
78 Status Reports Using SAS Dagnija V. Spuntelis dataset, length statement source 5
79 Teaching SAS Users in a Research Center Robert L. Clements proc step, by group processing, format statement, do over, anova source 6
80 Teaching Statistical Computing: A Comparison of Videotape and Classroom Methodologies John C. Boling proc step, data science, anova, proc glm, statistical software, sample, proc genmod source 4
81 Teaching the Three Major Statistical Packages Simultaneously - SAS, SPSS, BMDP Chapman P. Gleason The organizativTI of a three credi~ course in the use of the three major statistical soft ware packages (SAS, SPSS, and BMDP) is discussed in this paper. The organization of the course follows the steps needed to analyze a data set. Functional areas (such as trans formations or save files) for each package are contrasted. The course was offered for the first t~me in the fall of 1979 ~t the USDA Graduate School. a school for continuing educa tion located in Washington, D.C. Organization, materials, course experience. and ideas for improving the course are presented in addition to student evaluations of the course data science, statistical software source 6
82 The DISCRIM Procedure in SAS: A Comparative Analysis Emma L. Frazier discriminant analysis, cpu time, proc discrim, covariance, sample source 5
83 The Storage and Display of Geographical Information Using SAS Kenneth A. Hardy,
James D. Hosking,
Grady B. Meehan		 proc step, data science, regression analysis source 4
84 The Use of a Linear Spline Model in the Analysis of a Repeated Measures Experiment Through SAS Rocco L. Brunelle,
David W. Johnson		 regression analysis, proc reg, proc step source 5
85 Threshold Point Models: A SAS Macro for Estimation of Parameters George A. Milliken,
Jeanne L. Sebaugh		 confidence interval, predictor variable, standard deviation, response variable, macro definition, regression analysis source 6
86 Tobit Analysis Using SAS Cathryn Gust,
Rebecca Klemm		 response variable, proc reg source 5
87 Using SAS to Group Sets of Exponential Populations Curtis K. Church,
William J. Wilson		 sample, proc step source 3
88 Using Traffic Accident Reports for Improving Emergency Medical Services Resource Allocation Paul D. Krause proc sort, proc step, dataset, proc freq source 6

Back to Conf go 1981 go 1979

This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.

The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.

SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.